Cookie policy: This site uses cookies (small files stored on your computer) to simplify and improve your experience of this website. Cookies are small text files stored on the device you are using to access this website. For more information please take a look at our terms and conditions. Some parts of the site may not work properly if you choose not to accept cookies.

Abstract

The human aspects of the community pharmacy work system are vulnerable to medication-related errors. Established models of human error can identify actual or potential hazards, and are important in our understanding the interaction between human and system factors that influence performance. The software, hardware, environment and liveware (SHELL) model, a traditional human factors framework, is used in this article to classify potential sources of error in community pharmacies. A thorough review of the literature identified 50 risk factors which were categorised according to the dimensions of the SHELL model, which focuses on the system in which the pharmacist works, rather than individual performance. This model uses a systematic approach to examine mismatches at the interface between the human and the components of the system that are potential sources of error. The application of this model to hazard identification and error reduction in community pharmacy is explored.

Original submitted: 28 February 2017; Revised submitted: 28 May 2017; Accepted for publication: 30 May 2017.

Source: Jim Gibson / Alamy Stock Photo

Dispensing medications involves a number of repetitive, self-paced, interdependent sequential tasks, and mistakes can have potentially serious consequences. Established models of human error can identify actual or potential hazards, and are important in our understanding the interaction between human and system factors that influence performance. The software, hardware, environment and liveware (SHELL) model is used in this article to classify potential sources of error in community pharmacies.

Key points:

There is increasing interest in the factors that influence pharmacists’ performance and that have the potential to lead to medication errors.

The factors leading to an adverse medication incident usually go beyond the pharmacist who supplied the medication.

The SHELL model has not been previously used in the community pharmacy context, and has therefore been used here to categorise risk factors shown to contribute to medication errors, following a thorough literature review.

The SHELL model provides further awareness of system failures in error management.

Introduction

The performance of doctors, nurses, pharmacists and allied health professions is a key issue for healthcare regulators worldwide with increasing public demand for the competency of health professionals[1],[2],[3]. Pharmacists perform a variety of patient-orientated functions that require expert knowledge to achieve optimal medication safety and efficacy[1]. As pharmacists are the custodians of medication supply, there is increasing interest in the factors that influence their performance and that have the potential to lead to medication errors[4]. Previous literature suggests that pharmacists’ performance may be affected by multiple factors, including workplace circumstances, education, and personal attributes such as mental and physical health[4]. However, this remains an under-researched area. Certainly, limited attention has been given to the interplay between these factors and pharmacists’ performance, and ultimately, patient safety.

The dispensing error rate is 0.1% of dispensed items in community pharmacy in the UK[5],[6],[7]. While this incidence may appear low, with an average of 2.7 million prescriptions being dispensed daily in the UK[8], this amounts to around 2,700 errors each day. The rising number and complexity of available pharmaceutical products has increased the potential for adverse medication effects and serious interactions. A push to prevent such errors has led to a call for greater attention to the identification of factors that have the potential to undermine medication safety[9],[10],[11].

Around 20% of healthcare errors are related to problems with medicines; however, there is a need for an improved understanding about the incidence of, and factors contributing to, adverse incidents and near misses in community pharmacies[9],[12],[13],[14],[15],[16]. For instance, in Australia, like some other jurisdictions internationally, there is no national system for reporting medication errors[17],[18]. Another primary reason for the lack of clarity is that even where an incident-capturing system exists, the ‘blame culture’ and fear of disciplinary action by an employer or regulatory body, or fear of litigation, lead to under-reporting[16]. The Incident Decision Tree is one of a range of tools developed to provide a clearer framework for decision-making following a patient safety incident, and also prompts an awareness of system failures in error management[16].

Furthermore, the system-wide audit procedures and quality assurance initiatives that focus on safety in acute care environments are lacking in the community context, although an increasing number of studies are exploring this issue. Most recently, Phipps et al. used a combination of analytical methods to improve understanding of patient safety problems[19]. Medications were found to be almost always involved in the more than 14,000 incident reports analysed from the UK National Reporting and Learning System (NRLS), with dose/strength errors, incorrect medication and incorrect formulation cited as the most frequently occurring error types[19].

Dispensing medications involves a number of repetitive, self-paced, interdependent sequential tasks, and mistakes can have potentially serious consequences[20]. A body of research has demonstrated that the human aspects of a complex work system, such as a community pharmacy, are vulnerable and that errors can result from a dynamic interplay of factors[21],[22],[23]. One approach that has the potential to assist in the study of dispensing errors is to use established models of human error to help classify the interaction between human and system factors that negatively influence performance.

According to Garfield and Franklin, these models “can be helpful in determining why errors have occurred in the past, where future vulnerabilities may lie, and how healthcare professionals might take action to make clinical practice safer”[24]. In healthcare, the accident causation model[25] has been the predominant systems-based model applied to human error[24]. More recently, the Yorkshire contributory factors framework[26] has been developed; however, this relates to hospital settings only and is not used in the primary care context[24]. The software, hardware, environment and liveware (SHELL) model (see Figure 1) is an example of another human factors framework that has been used predominately in the aviation industry. More recently, it has also been used for risk assessment in healthcare collaborative settings and to analyse medical errors during surgical procedures; however, healthcare studies adopting SHELL are relatively scarce[22],[27].

Human factors research

One approach to learning from mistakes and preventing reoccurrence is through the analysis of the human and system factors that precipitate an event. Application of this analysis can improve safety and performance by optimising the fit between people and the systems they work in[21]. Human factors research involves the study of people’s performance in their living and working situations: their interactions with machines/instruments (hardware), procedures (software) and the environment around them, including relationships with other people that can affect the way they do their job[28].

Much of the application of human factors research has occurred in the aviation and defence industries. As aircrafts have become more reliable, humans have played an increasingly causal role in aviation accidents[29]. Human factors models have been widely used in collecting data about human performance and contributory component mismatches during aviation incident investigations, as recommended by the International Civil Aviation Organisation[30]. Further developments in the multidisciplinary science of human factors and ergonomics continue to shed light on the ways in which people interact with their environment[22].

The application of human factors research in surgery and in healthcare more broadly is gaining momentum[31]. The seminal report by the Institute of Medicine ‘To Err is Human: Building a Safer Health System’[32] highlighted not only the magnitude of medical errors but also the role of human factors in patient safety. This report concluded that, in addition to human factors, most healthcare errors are associated with faults in systems, processes or conditions that lead people to make mistakes or fail to prevent them[32].

The approach most commonly described in the literature to understand risks for medication error involves recording and then analysing isolated incidents[33],[34]. Therefore, traditionally, this human error problem has predominantly been viewed with a ‘person-centred’ approach, which focuses on the errors of individuals, such as inattention or moral weakness. Outcomes of this process often result in the perception that it is a particular individual, often the person closest to the last error, that should be ‘blamed’ for the adverse incident[16].

This reactive approach fails to account for the many ways in which errors can result from complex socio–technical systems in which health professionals work. While it is imperative to understand what risky activities are performed by individuals as part of their role, it is also essential to consider the role of the system using a methodical approach[33],[34]. A system approach concentrates on the conditions under which the individual works[24],[34]. Research shows that failures in the systems that individuals operate in and interact with are implicated in the majority of safety incidents[16] and, since the 1990s, a shift away from solely individualised blame has been advocated[24].

According to James Reason’s ‘Swiss cheese model’ for explaining how accidents happen, every step in medication prescribing, dispensing and the administering process has the potential for failure to varying degrees[33],[35]. Reason likens the typical healthcare system to a stack of Swiss cheese slices. Each hole is created by so-called ‘active failures’ of front-line individuals and ‘latent conditions’ related to the organisation, under which front-line individuals become dangerous. These holes provide an opportunity for failure to propagate, and each slice is a ‘defensive layer’ in the process. An error may allow a problem to pass through a hole in one layer, but in the next layer the holes are in different places and the problem should be caught. Each layer is a defence against the potential error. The fewer and smaller the holes, the more likely it is that an error will be caught or stopped. The challenge, according to Reason, is to fully understand all the factors that contribute to individual errors in a given situation or setting regardless of whether they are caught in a subsequent layer or not[34].

Root cause analysis (RCA) is one technique for seeking to understand the cause(s) and environmental context in which a particular incident occurred, using a systematic investigation of the factors that led to the error[14]. As RCAs have a retrospective approach, prevention of errors could be better achieved by examining the potential environmental, team and organisational factors as determinants of error[21],[22], referred to by Reason as ‘latent factors’ in his Swiss cheese model[25],[34]. A similar technique, as described by Sujan et al., uses a Plan-Do-Study-Act approach, which has been prototyped in a hospital dispensary environment, to identify organisational deficiencies that may cause latent conditions in the work environment[36].

Risk incident analysis has been increasingly used in a number of industries including aviation, defence and health. This work has highlighted hazard identification as the most important step in the overall prevention of errors. That is, we must first accurately identify an actual or potential hazard before it can be analysed and prevented[37]. This idea is supported by Hudson and Guchelaar, who proposed a risk assessment structure taken from approaches in other high-risk industries and adopted for pharmacy processes[33]. Additionally, Phipps et al. analysed various approaches for assessing hazards and identified key themes for further supporting a systematic approach to risk assessment in the pharmacy practice setting. This review recognises the importance of both considering the risks as well as the contribution of different stakeholder groups to these risks[3].

In aviation, accidents are usually highly visible and, as a result, the industry has developed sophisticated ways of analysing, documenting, disseminating information about, and preventing, errors[38],[39]. In health and pharmaceutical care, medication incidents and near misses are not always obvious, and there are not consistent procedures for identifying and reporting near misses. Healthcare is increasingly learning from and building on the human factors research conducted in aviation and other high-risk industries[38]. Vincent et al. emphasise that although a particular action or omission may be the immediate cause of an incident or error, a series of events and numerous contributing factors are often implicated in episodes where there is a departure from good practice[23],[31],[39]. Reporting on medication incidents and near misses can limit potential or actual reoccurrence of incidents, thereby managing the risk to the organisation and individual patients.

The SHELL model could be used in conjunction with the widely cited Reason model for analysing the contribution of human factors to medical error[22],[35]. This model has not yet been applied in the community pharmacy context and, therefore, the remainder of this paper will discuss the SHELL model as one approach to classify and help understand the involvement of human factors in medication-related errors, based on the systems philosophy of error management. The basic assumption with this approach is that humans make mistakes, and therefore, even in optimal organisations, errors are to be expected[24],[34].

The SHELL model

Since its development, the SHELL model has been modified to clarify the scope of factors in aviation systems that interact with the human operator, and different acronyms therefore exist in the literature to describe various evolutions of this model, as discussed below.

In 1972, Edwards stated that all aviation accidents are composed of four factors: software, hardware, environment and liveware. This original SHEL model allows for examination of the interactions between an individual and the other components of the system. Edwards further explained that each individual component (e.g. liveware) or a relationship between liveware and the other components (e.g. liveware–hardware, liveware–software or liveware–environment) is the source of all aviation accidents[21].

It was later identified that the original SHEL model did not encompass the interactive nature of the person–person relationship and, in 1993, Hawkins modified it to include the liveware–liveware relationship[40]. This became the SHELL model, illustrated in Figure 1. It is worth noting that the SHELL framework does not include the interfaces that do not involve human factors (e.g. hardware–environment)[38],[39]. The SHELL model depicts the inter-relationships evident in the working environment. The uneven edges indicate that the components of the system are constantly changing and will never match evenly; however, the aim is to minimise this mismatch between each of the system components.

Figure 1: Relationship between SHELL components

The software, hardware, environment and liveware (SHELL) model depicts the inter-relationships evident in the working environment. The uneven edges indicate that the components of the system are constantly changing and will never match evenly; however, the aim is to minimise this mismatch between each of the system components.

In the decade following the development of this model, research by Hofstede provided information on the cultural influences at the human–systems interface. This cultural dimension became an add-on element, and the SCHELL model incorporates differences in behaviour, values and expectations related to ethnic group and cultural perspectives[41].

Later, emphasis on individual level factors that were responsible for aviation incidents shifted, and the focus expanded to include organisational factors. To better categorise organisational risk issues, the SHELL model was again modified, and a new component was added to consider the interaction between humans and the organisation in which they work, represented by the acronym SHELLO[37]. For the purpose of this discussion, we will use the system components represented by the SHELLO model. In this article, SHELL is used to reflect the most common reference to the model in the literature.

Liveware (L)

The centre of the model, represented by the L, is the most critical person and the most flexible component of the system. In a community pharmacy, for example, the individual in this central role might be a pharmacist or another individual who holds a position essential to patient care. The liveware is the hub of the SHELL model and the remaining components must be matched to this central component. This matching is influenced by a number of intrinsic factors such as physical characteristics, knowledge, information processing, communication style, personality, environmental tolerances, cognitive and psychomotor skills, and attitudes. It is the interaction of these intrinsic factors with other components of the system that make the role of the central human operator subject to many variations in performance[39]. More information on how this component can be applied to the community pharmacy setting can be found in Table 1.

Liveware–hardware (L–H)

Much of the science of ergonomics is concerned with the liveware–hardware interface that is the design of equipment to fit the characteristics of the human body. The introduction of barcode scanners and automated dispensing systems represent examples of how this interface can be enhanced to improve patient safety in community pharmacies. There is a natural tendency for humans to adapt to L–H mismatches by using techniques and strategies to overcome potential hazards. This adaptation is often seen in experienced staff who have developed a wealth of specific knowledge around the craft of dispensing medications, whereas junior staff remain vulnerable to the risks posed by poor equipment design[39]. More information on how this component can be applied to the community pharmacy setting can be found in Table 2.

Liveware–software (L–S)

The liveware–software interface represents the interaction between humans and the non-physical components of the system, such as procedures, checklists, workplace norms and computer programmes. Pharmacists are influenced by their interaction with the legislative regulation of medicines and pharmacy practice that governs the profession[42]. Internationally, pharmacists are also required to meet minimum standards that ensure they are up to date and fit to practice, with the ultimate aim of ensuring public trust and safety[4]. For example, in Australia, pharmacists must deliver services to consumers, other healthcare professionals and the community in a way that is consistent with the professional codes and standards specified in the Pharmaceutical Society of Australia Code of Professional Conduct, the Society of Hospital Pharmacists of Australia Code of Ethics, the Medicines Australia Code of Conduct, and The National Competency Standards Framework for Pharmacists in Australia 2010[43]. In the UK, all pharmacists registered with the General Pharmaceutical Council are required to practice in accordance with the Standards of Conduct, Ethics and Performance, which define an acceptable level of performance that is “consistently at or above the minimum standards accepted by the profession”[4],[44],[45]. More information on how this component can be applied to the community pharmacy setting can be found in Table 3.

Liveware–environment (L–E)

The liveware–environment interface represents the interaction of humans and their environment. The community pharmacy model is a unique healthcare environment that combines the delivery of patient-centric, high quality cost-effective health services with the corporate aspects of a retail environment and the need to maintain a viable business. In Australia, there are just over 6,500 community pharmacies (23.1 community pharmacies per 1 million population). This compares with 11,495 community pharmacies in the UK (22.1 pharmacies per 1 million population) located in metropolitan, suburban, rural and remote regions[8],[13],[46]. This brings with it a number of environmental challenges that can influence patient safety, including noise, overcrowding, consumer demand, angry or aggressive patients and relatives, and frequent disturbances and interruptions. Commonly reported causes of error in community pharmacy relate to pharmacist workload, particularly at times when the pharmacy is busy, leaving them vulnerable to becoming frequently interrupted during the process of checking prescriptions and dispensing medications. Research by Buchanan et al. shows there is a direct correlation between pharmacist error rate and their corresponding daily prescription workload[47]. Furthermore, interruptions and distractions have been shown to have a direct association with dispensing errors, particularly those that involved incorrect label information[48]. More information on how this component can be applied to the community pharmacy setting can be found in Table 4.

Liveware–liveware (L–L)

Liveware–liveware represents the interface between two people and can be referred to as interpersonal communication. Community pharmacies are the most frequently accessed of all healthcare providers, and pharmacists are required to communicate with various individuals as part of their role, including consumers and their relatives, prescribers, specialists, dispensary technicians, pharmacy staff and other healthcare professionals[49]. Effective operation of a community pharmacy requires high-level skills in interpersonal communication, interprofessional collaboration and teamwork, leadership and followership[50]. The liveware–liveware interaction is central to the maintenance and continuity of information flow in patient care and to optimising patient outcomes. More information on how this component can be applied to the community pharmacy setting can be found in Table 5.

Liveware–organisation (L–O)

The liveware–organisation interface is concerned with the interaction between the controller and the organisational aspects of the system, including workload allocation, organisational structure, political environment, financial constraints, resource management, safety culture and training opportunities[39]. Community pharmacy business owners who fail to appropriately match tasks with employees’ skills and abilities, or fail to adequately distribute workload, can have a negative impact on patient safety[14],[51]. For example, requirements for excessive prescription numbers for an individual pharmacist and inadequate time for patient counselling can be a precursor to potential human error and, in turn, medication misadventure[4],[47],[52]. More information on how this component can be applied to the community pharmacy setting can be found in Table 6.

Application of the SHELL framework in the community pharmacy domain

Sources and selection criteria

A thorough review of relevant, peer-reviewed literature was conducted to identify factors that contribute to medication errors.

In Tables 1–6, the components of the SHELLO model are applied to a community pharmacy setting, with a detailed overview of the factors that influence medication safety by community pharmacists in medication supply. Risk factors were derived from six review articles and fourteen original research studies, described in Table 7, which were identified from an extensive literature search. The literature search identified articles published in English after 1990 that reported on factors affecting pharmacist performance and/or contributing to medication errors in community pharmacy. Current evidence-based competency guidelines and practice standards, as well as guidelines from the Institute of Safe Medication Practices[53], were also considered in the review. The 50 identified risk factors have been categorised within dimensions of the modified SHELLO model and have been shown to contribute to medication errors either alone or in combination with other factors in community pharmacy.

Table 1: Liveware (L)

Core capacity/intrinsic characteristics of a pharmacist

Risk factor

Description/example

Source (see Table 7 for more details)

1. Attitude towards safety

The likelihood that someone will engage in behaviours that reduce risk.

Poor design may lead to accidents (e.g. broken glassware). Inadvertent cross contamination (e.g. use of same measuring cylinder for methadone and water without appropriate cleaning measures). Cross contamination when preparing penicillin mixtures.

The physical layout of the pharmacy may require the pharmacist to move and orientate themselves to the new location several times during a task, which may negatively impact pharmacist task completion and increase workload; clutter in the pharmacy.

Organisation does not give appropriate priority to safety and does not manage safety issues appropriately. Safety climate is influenced by level of job demands and resources available to meet these demands.

Evaluation of medication errors in community pharmacy settings. A retrospective report.

Research report/qualitative review of data to identify incidence and contributing factors to medication dispensing errors in community pharmacy reported to Board of Pharmacy in New Hampshire, United States.

When procedures meet practice in community pharmacies: qualitative insights from pharmacists and pharmacy support staff.

Empirical research study/qualitative thematic analysis of community pharmacist interviews that focused on opinions about procedures that they are expected to follow in their role.

Discussion

Medication dispensing errors present a significant risk to the public. The factors leading to an adverse medication incident usually go beyond the pharmacist who supplied the medication at the time. A medication supply error is described in Box 1. This is one example to demonstrate how the SHELL model can be used to reveal error-prone processes in medication supply in community pharmacies, using information retrospectively from a published adverse incident. A detailed analysis of the incident[63] identifying the immediate causes of the error are discussed below with the appropriate SHELL allocations, which enables a focus on the system in which the pharmacist worked rather than the individual. It is envisaged that the SHELL model could be equally applied to hazard identification in community pharmacy microsystems, as well as secondary analysis to examine human factors issues in other errors or near-miss incidents.

Box 1: Example of a medication supply error

A customer presented a script for naproxen (a non-steroidal anti-inflammatory medication) that was prescribed to treat his gout. The patient had planned a holiday and requested enough of the medication to last for his entire trip. The pharmacist incorrectly labelled and dispensed two bottles of methyldopa (an antihypertensive) to the customer instead of naproxen. While on holiday, the customer suffered a severe attack of gout and took the medication that had been dispensed. His gout did not improve, and his pain increased despite taking increasing doses of the medication over several days. The customer was eventually admitted to hospital because of the severity of his gout, and the admitting doctor identified the medication error. The customer’s gout attack was so severe that the muscles in his left leg did not recover for 12 months. The inadvertent use of methyldopa had no long-term adverse effects on the patient.

Liveware–hardware mismatch: The bottle of methyldopa was incorrectly placed in the dispensing shelves instead of naproxen. Although the correct medication (naproxen) was electronically dispensed to the correct patient using dispensing software, the wrong product was subsequently selected from the shelf. The similarity of labelling and packaging contributed to the error as the bottles for naproxen and methyldopa were similar in size and colour.

Liveware–software mismatch: Protocols for checking and supplying medications, including barcode scanning, in the pharmacy were inadequate, particularly for similar-sounding drug names or those with similar packaging, and the process for checking the dispensed medication was not completed adequately.

Liveware–liveware mismatch: There was no communication between the pharmacist and the patient, even though it was the first time that he had been supplied with the medication. Providing information to the customer at the time of supply could have identified any error at this point.

Liveware–organisation mismatch: Additional staff were required to cope with workload in the pharmacy. There was a lack of control over pharmacy operations, including procedures for placing medications on the shelf when an order was received from the supplier, or dispensing tasks by non-pharmacist personnel.

Like Reason’s accident causation model[25] and the Yorkshire contributory factors framework[26], the SHELL model helps to illustrate how important it is to understand the human–system interactions in an adverse incident, rather than simply labelling an individual’s actions as ‘front-end operator error’. One advantage of using a framework like the SHELL model is that it considers the full range of potential elements of the working environment and the way they interact with humans. The interfaces between SHELL elements define key areas of analysis, providing an underlying structure for risk assessment. A study conducted by Antunes et al. showed that the SHELL model is adequate to analyse the complex issues raised by healthcare collaborative settings[27], and the methodical approach that the SHELL model provides can help guard against overlooking potential sources of error. Additionally, the model focuses attention on human factors that are relevant to healthcare microsystems. Human factor approaches that emphasise the role of the broader macrosystem or organisation may prevent the detection of more proximal determinants of error, which the SHELL model has the potential to do[22].

Conclusion

The goal of every community pharmacy should be to ensure the safest and highest quality care possible for patients through continual improvements in the system of medication use. To achieve this, community pharmacists and regulating bodies must identify actual and potential risks so that profession-wide strategies are implemented to improve the system and minimise patient harm, including the development of innovative training approaches.

The SHELL model provides a useful systems-based approach for supporting our understanding of factors that influence medication safety in the community pharmacy. While empirical studies are lacking, this paper describes how the application of the SHELL model can reveal determinants of error that can be targeted towards interventions to improve patient safety. The insights from this paper will be relevant to practising pharmacists and business owners, as well as education providers and regulatory bodies charged with the responsibility for preparing future pharmacists.

Author disclosures and conflicts of interest:

The authors have no relevant affiliations or financial involvement with any organisation or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. No writing assistance was used in the production of this manuscript.

Reading this article counts towards your CPD

You can use the following forms to record your learning and action points from this article from Salvadore Publications.

Your CPD module results are stored against your account here at The Salvadore. You must be registered and logged into the site to do this. To review your module results, go to the ‘My Account’ tab and then ‘My CPD’.

Any training, learning or development activities that you undertake for CPD can also be recorded as evidence as part of your RPS Faculty practice-based portfolio when preparing for Faculty membership. To start your RPS Faculty journey today, access the portfolio and tools at

[5] NHS National Patient Safety Agency. Design for patient safety: a guide to the design of the dispensing environment. London: The National Patient Safety Agency 2007. Available at: (accessed July 2017)

[6] Roughead E & Semple S. Second national report on patient safety: improving medication safety. Australian Council for Safety and Quality in Health Care; 2002. Available at: (accessed July 2017)

[11] Roughead E & Semple S. Medication safety in acute care in Australia: where are we now? Part 1: a review of the extent and causes of medication problems 2002–2008. Aust New Zealand Health Policy 2009;6(18).

[12] The Pharmacy Guild of Australia. Discussion paper on achieving the directions established in the National Quality and Safety Framework. Canberra, ACT2008.

[13] The Pharmacy Guild of Australia. Serving Australians: a system of community pharmacy. Canberra, 2012.